Overview

Dataset statistics

Number of variables24
Number of observations8240
Missing cells12563
Missing cells (%)6.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.1 MiB
Average record size in memory780.5 B

Variable types

Numeric13
Categorical10
Boolean1

Alerts

cons.conf.idx is highly overall correlated with monthHigh correlation
cons.price.idx is highly overall correlated with contact and 2 other fieldsHigh correlation
contact is highly overall correlated with cons.price.idx and 1 other fieldsHigh correlation
emp.var.rate is highly overall correlated with cons.price.idx and 3 other fieldsHigh correlation
euribor3m is highly overall correlated with emp.var.rate and 2 other fieldsHigh correlation
housing is highly overall correlated with loanHigh correlation
id is highly overall correlated with respondedHigh correlation
loan is highly overall correlated with housing and 1 other fieldsHigh correlation
month is highly overall correlated with cons.conf.idx and 4 other fieldsHigh correlation
nr.employed is highly overall correlated with emp.var.rate and 1 other fieldsHigh correlation
pastEmail is highly overall correlated with previousHigh correlation
pdays is highly overall correlated with pmonths and 2 other fieldsHigh correlation
pmonths is highly overall correlated with pdays and 2 other fieldsHigh correlation
poutcome is highly overall correlated with pdays and 2 other fieldsHigh correlation
previous is highly overall correlated with pastEmail and 3 other fieldsHigh correlation
profit is highly overall correlated with loan and 2 other fieldsHigh correlation
responded is highly overall correlated with id and 1 other fieldsHigh correlation
schooling is highly overall correlated with profitHigh correlation
default is highly imbalanced (54.8%)Imbalance
loan is highly imbalanced (51.3%)Imbalance
poutcome is highly imbalanced (55.5%)Imbalance
custAge has 2016 (24.5%) missing valuesMissing
schooling has 2408 (29.2%) missing valuesMissing
day_of_week has 789 (9.6%) missing valuesMissing
profit has 7310 (88.7%) missing valuesMissing
profit is highly skewed (γ1 = -30.40426657)Skewed
id is uniformly distributedUniform
previous has 7060 (85.7%) zerosZeros
pastEmail has 7219 (87.6%) zerosZeros

Reproduction

Analysis started2024-09-05 16:00:01.850015
Analysis finished2024-09-05 16:01:16.330246
Duration1 minute and 14.48 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

custAge
Real number (ℝ)

MISSING 

Distinct72
Distinct (%)1.2%
Missing2016
Missing (%)24.5%
Infinite0
Infinite (%)0.0%
Mean39.953728
Minimum18
Maximum94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size64.5 KiB
2024-09-05T21:31:16.775991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile26
Q132
median38
Q347
95-th percentile58
Maximum94
Range76
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.540516
Coefficient of variation (CV)0.26381808
Kurtosis1.0535234
Mean39.953728
Median Absolute Deviation (MAD)7
Skewness0.86627065
Sum248672
Variance111.10247
MonotonicityNot monotonic
2024-09-05T21:31:17.272753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31 300
 
3.6%
33 299
 
3.6%
30 275
 
3.3%
32 269
 
3.3%
34 262
 
3.2%
35 254
 
3.1%
36 253
 
3.1%
29 229
 
2.8%
37 229
 
2.8%
38 226
 
2.7%
Other values (62) 3628
44.0%
(Missing) 2016
24.5%
ValueCountFrequency (%)
18 4
 
< 0.1%
19 6
 
0.1%
20 9
 
0.1%
21 14
 
0.2%
22 25
 
0.3%
23 31
 
0.4%
24 73
0.9%
25 87
1.1%
26 100
1.2%
27 130
1.6%
ValueCountFrequency (%)
94 1
 
< 0.1%
91 1
 
< 0.1%
88 1
 
< 0.1%
86 1
 
< 0.1%
85 5
0.1%
84 2
 
< 0.1%
83 3
 
< 0.1%
82 5
0.1%
81 5
0.1%
80 10
0.1%

profession
Categorical

Distinct12
Distinct (%)0.1%
Missing2
Missing (%)< 0.1%
Memory size530.8 KiB
admin.
2102 
blue-collar
1847 
technician
1351 
services
792 
management
583 
Other values (7)
1563 

Length

Max length13
Median length12
Mean length8.9567856
Min length6

Characters and Unicode

Total characters73786
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowadmin.
2nd rowservices
3rd rowadmin.
4th rowadmin.
5th rowblue-collar

Common Values

ValueCountFrequency (%)
admin. 2102
25.5%
blue-collar 1847
22.4%
technician 1351
16.4%
services 792
 
9.6%
management 583
 
7.1%
retired 337
 
4.1%
entrepreneur 314
 
3.8%
self-employed 279
 
3.4%
housemaid 213
 
2.6%
unemployed 190
 
2.3%
Other values (2) 230
 
2.8%

Length

2024-09-05T21:31:17.761980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
admin 2102
25.5%
blue-collar 1847
22.4%
technician 1351
16.4%
services 792
 
9.6%
management 583
 
7.1%
retired 337
 
4.1%
entrepreneur 314
 
3.8%
self-employed 279
 
3.4%
housemaid 213
 
2.6%
unemployed 190
 
2.3%
Other values (2) 230
 
2.8%

Most occurring characters

ValueCountFrequency (%)
e 9467
12.8%
n 7160
 
9.7%
a 6679
 
9.1%
l 6289
 
8.5%
i 6146
 
8.3%
c 5341
 
7.2%
r 4255
 
5.8%
m 3950
 
5.4%
d 3280
 
4.4%
t 2903
 
3.9%
Other values (14) 18316
24.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 69558
94.3%
Dash Punctuation 2126
 
2.9%
Other Punctuation 2102
 
2.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 9467
13.6%
n 7160
10.3%
a 6679
9.6%
l 6289
9.0%
i 6146
8.8%
c 5341
 
7.7%
r 4255
 
6.1%
m 3950
 
5.7%
d 3280
 
4.7%
t 2903
 
4.2%
Other values (12) 14088
20.3%
Dash Punctuation
ValueCountFrequency (%)
- 2126
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2102
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 69558
94.3%
Common 4228
 
5.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 9467
13.6%
n 7160
10.3%
a 6679
9.6%
l 6289
9.0%
i 6146
8.8%
c 5341
 
7.7%
r 4255
 
6.1%
m 3950
 
5.7%
d 3280
 
4.7%
t 2903
 
4.2%
Other values (12) 14088
20.3%
Common
ValueCountFrequency (%)
- 2126
50.3%
. 2102
49.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 73786
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 9467
12.8%
n 7160
 
9.7%
a 6679
 
9.1%
l 6289
 
8.5%
i 6146
 
8.3%
c 5341
 
7.2%
r 4255
 
5.8%
m 3950
 
5.4%
d 3280
 
4.4%
t 2903
 
3.9%
Other values (14) 18316
24.8%

marital
Categorical

Distinct4
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size513.7 KiB
married
4957 
single
2339 
divorced
932 
unknown
 
10

Length

Max length8
Median length7
Mean length6.8292061
Min length6

Characters and Unicode

Total characters56259
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsingle
2nd rowsingle
3rd rowsingle
4th rowdivorced
5th rowsingle

Common Values

ValueCountFrequency (%)
married 4957
60.2%
single 2339
28.4%
divorced 932
 
11.3%
unknown 10
 
0.1%
(Missing) 2
 
< 0.1%

Length

2024-09-05T21:31:18.211407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-05T21:31:18.622330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
married 4957
60.2%
single 2339
28.4%
divorced 932
 
11.3%
unknown 10
 
0.1%

Most occurring characters

ValueCountFrequency (%)
r 10846
19.3%
i 8228
14.6%
e 8228
14.6%
d 6821
12.1%
m 4957
8.8%
a 4957
8.8%
n 2369
 
4.2%
s 2339
 
4.2%
g 2339
 
4.2%
l 2339
 
4.2%
Other values (6) 2836
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 56259
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 10846
19.3%
i 8228
14.6%
e 8228
14.6%
d 6821
12.1%
m 4957
8.8%
a 4957
8.8%
n 2369
 
4.2%
s 2339
 
4.2%
g 2339
 
4.2%
l 2339
 
4.2%
Other values (6) 2836
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 56259
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 10846
19.3%
i 8228
14.6%
e 8228
14.6%
d 6821
12.1%
m 4957
8.8%
a 4957
8.8%
n 2369
 
4.2%
s 2339
 
4.2%
g 2339
 
4.2%
l 2339
 
4.2%
Other values (6) 2836
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 56259
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 10846
19.3%
i 8228
14.6%
e 8228
14.6%
d 6821
12.1%
m 4957
8.8%
a 4957
8.8%
n 2369
 
4.2%
s 2339
 
4.2%
g 2339
 
4.2%
l 2339
 
4.2%
Other values (6) 2836
 
5.0%

schooling
Categorical

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)0.1%
Missing2408
Missing (%)29.2%
Memory size472.4 KiB
university.degree
1731 
high.school
1340 
basic.9y
863 
professional.course
738 
basic.4y
586 
Other values (3)
574 

Length

Max length19
Median length17
Mean length12.708333
Min length7

Characters and Unicode

Total characters74115
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowuniversity.degree
2nd rowhigh.school
3rd rowhigh.school
4th rowuniversity.degree
5th rowprofessional.course

Common Values

ValueCountFrequency (%)
university.degree 1731
21.0%
high.school 1340
16.3%
basic.9y 863
 
10.5%
professional.course 738
 
9.0%
basic.4y 586
 
7.1%
basic.6y 313
 
3.8%
unknown 260
 
3.2%
illiterate 1
 
< 0.1%
(Missing) 2408
29.2%

Length

2024-09-05T21:31:19.033378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-05T21:31:19.426885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
university.degree 1731
29.7%
high.school 1340
23.0%
basic.9y 863
14.8%
professional.course 738
12.7%
basic.4y 586
 
10.0%
basic.6y 313
 
5.4%
unknown 260
 
4.5%
illiterate 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 8402
 
11.3%
i 7304
 
9.9%
s 7047
 
9.5%
. 5571
 
7.5%
o 5154
 
7.0%
r 4939
 
6.7%
h 4020
 
5.4%
c 3840
 
5.2%
y 3493
 
4.7%
n 3249
 
4.4%
Other values (15) 21096
28.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 66782
90.1%
Other Punctuation 5571
 
7.5%
Decimal Number 1762
 
2.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 8402
12.6%
i 7304
10.9%
s 7047
10.6%
o 5154
 
7.7%
r 4939
 
7.4%
h 4020
 
6.0%
c 3840
 
5.8%
y 3493
 
5.2%
n 3249
 
4.9%
g 3071
 
4.6%
Other values (11) 16263
24.4%
Decimal Number
ValueCountFrequency (%)
9 863
49.0%
4 586
33.3%
6 313
 
17.8%
Other Punctuation
ValueCountFrequency (%)
. 5571
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 66782
90.1%
Common 7333
 
9.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 8402
12.6%
i 7304
10.9%
s 7047
10.6%
o 5154
 
7.7%
r 4939
 
7.4%
h 4020
 
6.0%
c 3840
 
5.8%
y 3493
 
5.2%
n 3249
 
4.9%
g 3071
 
4.6%
Other values (11) 16263
24.4%
Common
ValueCountFrequency (%)
. 5571
76.0%
9 863
 
11.8%
4 586
 
8.0%
6 313
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74115
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 8402
 
11.3%
i 7304
 
9.9%
s 7047
 
9.5%
. 5571
 
7.5%
o 5154
 
7.0%
r 4939
 
6.7%
h 4020
 
5.4%
c 3840
 
5.2%
y 3493
 
4.7%
n 3249
 
4.4%
Other values (15) 21096
28.5%

default
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size482.7 KiB
no
6619 
unknown
1618 
yes
 
1

Length

Max length7
Median length2
Mean length2.9821559
Min length2

Characters and Unicode

Total characters24567
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowunknown
5th rowunknown

Common Values

ValueCountFrequency (%)
no 6619
80.3%
unknown 1618
 
19.6%
yes 1
 
< 0.1%
(Missing) 2
 
< 0.1%

Length

2024-09-05T21:31:19.907987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-05T21:31:20.275911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
no 6619
80.3%
unknown 1618
 
19.6%
yes 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n 11473
46.7%
o 8237
33.5%
u 1618
 
6.6%
k 1618
 
6.6%
w 1618
 
6.6%
y 1
 
< 0.1%
e 1
 
< 0.1%
s 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 24567
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 11473
46.7%
o 8237
33.5%
u 1618
 
6.6%
k 1618
 
6.6%
w 1618
 
6.6%
y 1
 
< 0.1%
e 1
 
< 0.1%
s 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 24567
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 11473
46.7%
o 8237
33.5%
u 1618
 
6.6%
k 1618
 
6.6%
w 1618
 
6.6%
y 1
 
< 0.1%
e 1
 
< 0.1%
s 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24567
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 11473
46.7%
o 8237
33.5%
u 1618
 
6.6%
k 1618
 
6.6%
w 1618
 
6.6%
y 1
 
< 0.1%
e 1
 
< 0.1%
s 1
 
< 0.1%

housing
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size479.9 KiB
yes
4300 
no
3754 
unknown
 
184

Length

Max length7
Median length3
Mean length2.6336489
Min length2

Characters and Unicode

Total characters21696
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowyes
5th rowyes

Common Values

ValueCountFrequency (%)
yes 4300
52.2%
no 3754
45.6%
unknown 184
 
2.2%
(Missing) 2
 
< 0.1%

Length

2024-09-05T21:31:20.649356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-05T21:31:20.996418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
yes 4300
52.2%
no 3754
45.6%
unknown 184
 
2.2%

Most occurring characters

ValueCountFrequency (%)
n 4306
19.8%
y 4300
19.8%
e 4300
19.8%
s 4300
19.8%
o 3938
18.2%
u 184
 
0.8%
k 184
 
0.8%
w 184
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 21696
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 4306
19.8%
y 4300
19.8%
e 4300
19.8%
s 4300
19.8%
o 3938
18.2%
u 184
 
0.8%
k 184
 
0.8%
w 184
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 21696
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 4306
19.8%
y 4300
19.8%
e 4300
19.8%
s 4300
19.8%
o 3938
18.2%
u 184
 
0.8%
k 184
 
0.8%
w 184
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21696
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 4306
19.8%
y 4300
19.8%
e 4300
19.8%
s 4300
19.8%
o 3938
18.2%
u 184
 
0.8%
k 184
 
0.8%
w 184
 
0.8%

loan
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size477.0 KiB
no
6775 
yes
1279 
unknown
 
184

Length

Max length7
Median length2
Mean length2.2669337
Min length2

Characters and Unicode

Total characters18675
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowyes
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 6775
82.2%
yes 1279
 
15.5%
unknown 184
 
2.2%
(Missing) 2
 
< 0.1%

Length

2024-09-05T21:31:21.382908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-05T21:31:21.731541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
no 6775
82.2%
yes 1279
 
15.5%
unknown 184
 
2.2%

Most occurring characters

ValueCountFrequency (%)
n 7327
39.2%
o 6959
37.3%
y 1279
 
6.8%
e 1279
 
6.8%
s 1279
 
6.8%
u 184
 
1.0%
k 184
 
1.0%
w 184
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 18675
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 7327
39.2%
o 6959
37.3%
y 1279
 
6.8%
e 1279
 
6.8%
s 1279
 
6.8%
u 184
 
1.0%
k 184
 
1.0%
w 184
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 18675
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 7327
39.2%
o 6959
37.3%
y 1279
 
6.8%
e 1279
 
6.8%
s 1279
 
6.8%
u 184
 
1.0%
k 184
 
1.0%
w 184
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18675
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 7327
39.2%
o 6959
37.3%
y 1279
 
6.8%
e 1279
 
6.8%
s 1279
 
6.8%
u 184
 
1.0%
k 184
 
1.0%
w 184
 
1.0%

contact
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size526.0 KiB
cellular
5243 
telephone
2995 

Length

Max length9
Median length8
Mean length8.3635591
Min length8

Characters and Unicode

Total characters68899
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcellular
2nd rowcellular
3rd rowtelephone
4th rowcellular
5th rowcellular

Common Values

ValueCountFrequency (%)
cellular 5243
63.6%
telephone 2995
36.3%
(Missing) 2
 
< 0.1%

Length

2024-09-05T21:31:22.140203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-05T21:31:22.496330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
cellular 5243
63.6%
telephone 2995
36.4%

Most occurring characters

ValueCountFrequency (%)
l 18724
27.2%
e 14228
20.7%
c 5243
 
7.6%
u 5243
 
7.6%
a 5243
 
7.6%
r 5243
 
7.6%
t 2995
 
4.3%
p 2995
 
4.3%
h 2995
 
4.3%
o 2995
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 68899
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 18724
27.2%
e 14228
20.7%
c 5243
 
7.6%
u 5243
 
7.6%
a 5243
 
7.6%
r 5243
 
7.6%
t 2995
 
4.3%
p 2995
 
4.3%
h 2995
 
4.3%
o 2995
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 68899
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 18724
27.2%
e 14228
20.7%
c 5243
 
7.6%
u 5243
 
7.6%
a 5243
 
7.6%
r 5243
 
7.6%
t 2995
 
4.3%
p 2995
 
4.3%
h 2995
 
4.3%
o 2995
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68899
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 18724
27.2%
e 14228
20.7%
c 5243
 
7.6%
u 5243
 
7.6%
a 5243
 
7.6%
r 5243
 
7.6%
t 2995
 
4.3%
p 2995
 
4.3%
h 2995
 
4.3%
o 2995
 
4.3%

month
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)0.1%
Missing2
Missing (%)< 0.1%
Memory size482.9 KiB
may
2814 
jul
1352 
aug
1241 
jun
1055 
nov
813 
Other values (5)
963 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters24714
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowapr
2nd rowjul
3rd rowjun
4th rowjul
5th rowjul

Common Values

ValueCountFrequency (%)
may 2814
34.2%
jul 1352
16.4%
aug 1241
15.1%
jun 1055
 
12.8%
nov 813
 
9.9%
apr 551
 
6.7%
oct 156
 
1.9%
sep 121
 
1.5%
mar 106
 
1.3%
dec 29
 
0.4%
(Missing) 2
 
< 0.1%

Length

2024-09-05T21:31:22.843738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-05T21:31:23.269153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
may 2814
34.2%
jul 1352
16.4%
aug 1241
15.1%
jun 1055
 
12.8%
nov 813
 
9.9%
apr 551
 
6.7%
oct 156
 
1.9%
sep 121
 
1.5%
mar 106
 
1.3%
dec 29
 
0.4%

Most occurring characters

ValueCountFrequency (%)
a 4712
19.1%
u 3648
14.8%
m 2920
11.8%
y 2814
11.4%
j 2407
9.7%
n 1868
 
7.6%
l 1352
 
5.5%
g 1241
 
5.0%
o 969
 
3.9%
v 813
 
3.3%
Other values (7) 1970
8.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 24714
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 4712
19.1%
u 3648
14.8%
m 2920
11.8%
y 2814
11.4%
j 2407
9.7%
n 1868
 
7.6%
l 1352
 
5.5%
g 1241
 
5.0%
o 969
 
3.9%
v 813
 
3.3%
Other values (7) 1970
8.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 24714
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 4712
19.1%
u 3648
14.8%
m 2920
11.8%
y 2814
11.4%
j 2407
9.7%
n 1868
 
7.6%
l 1352
 
5.5%
g 1241
 
5.0%
o 969
 
3.9%
v 813
 
3.3%
Other values (7) 1970
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24714
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 4712
19.1%
u 3648
14.8%
m 2920
11.8%
y 2814
11.4%
j 2407
9.7%
n 1868
 
7.6%
l 1352
 
5.5%
g 1241
 
5.0%
o 969
 
3.9%
v 813
 
3.3%
Other values (7) 1970
8.0%

day_of_week
Categorical

MISSING 

Distinct5
Distinct (%)0.1%
Missing789
Missing (%)9.6%
Memory size461.4 KiB
mon
1598 
thu
1533 
tue
1478 
wed
1473 
fri
1369 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters22353
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowwed
2nd rowthu
3rd rowtue
4th rowtue
5th rowthu

Common Values

ValueCountFrequency (%)
mon 1598
19.4%
thu 1533
18.6%
tue 1478
17.9%
wed 1473
17.9%
fri 1369
16.6%
(Missing) 789
9.6%

Length

2024-09-05T21:31:23.748580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-05T21:31:24.127782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
mon 1598
21.4%
thu 1533
20.6%
tue 1478
19.8%
wed 1473
19.8%
fri 1369
18.4%

Most occurring characters

ValueCountFrequency (%)
t 3011
13.5%
u 3011
13.5%
e 2951
13.2%
m 1598
7.1%
o 1598
7.1%
n 1598
7.1%
h 1533
6.9%
w 1473
6.6%
d 1473
6.6%
f 1369
6.1%
Other values (2) 2738
12.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 22353
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 3011
13.5%
u 3011
13.5%
e 2951
13.2%
m 1598
7.1%
o 1598
7.1%
n 1598
7.1%
h 1533
6.9%
w 1473
6.6%
d 1473
6.6%
f 1369
6.1%
Other values (2) 2738
12.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 22353
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 3011
13.5%
u 3011
13.5%
e 2951
13.2%
m 1598
7.1%
o 1598
7.1%
n 1598
7.1%
h 1533
6.9%
w 1473
6.6%
d 1473
6.6%
f 1369
6.1%
Other values (2) 2738
12.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 3011
13.5%
u 3011
13.5%
e 2951
13.2%
m 1598
7.1%
o 1598
7.1%
n 1598
7.1%
h 1533
6.9%
w 1473
6.6%
d 1473
6.6%
f 1369
6.1%
Other values (2) 2738
12.2%

campaign
Real number (ℝ)

Distinct34
Distinct (%)0.4%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2.5316824
Minimum1
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size64.5 KiB
2024-09-05T21:31:24.584659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile7
Maximum40
Range39
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.7097733
Coefficient of variation (CV)1.0703449
Kurtosis37.294564
Mean2.5316824
Median Absolute Deviation (MAD)1
Skewness4.8239427
Sum20856
Variance7.3428713
MonotonicityNot monotonic
2024-09-05T21:31:25.068809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
1 3542
43.0%
2 2159
26.2%
3 1045
 
12.7%
4 542
 
6.6%
5 285
 
3.5%
6 205
 
2.5%
7 113
 
1.4%
8 73
 
0.9%
9 70
 
0.8%
11 42
 
0.5%
Other values (24) 162
 
2.0%
ValueCountFrequency (%)
1 3542
43.0%
2 2159
26.2%
3 1045
 
12.7%
4 542
 
6.6%
5 285
 
3.5%
6 205
 
2.5%
7 113
 
1.4%
8 73
 
0.9%
9 70
 
0.8%
10 40
 
0.5%
ValueCountFrequency (%)
40 1
 
< 0.1%
39 1
 
< 0.1%
35 1
 
< 0.1%
34 1
 
< 0.1%
33 2
< 0.1%
31 1
 
< 0.1%
30 3
< 0.1%
29 2
< 0.1%
27 4
< 0.1%
26 2
< 0.1%

pdays
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)0.3%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean960.91661
Minimum0
Maximum999
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size64.5 KiB
2024-09-05T21:31:25.511191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile999
Q1999
median999
Q3999
95-th percentile999
Maximum999
Range999
Interquartile range (IQR)0

Descriptive statistics

Standard deviation190.69505
Coefficient of variation (CV)0.1984512
Kurtosis21.124715
Mean960.91661
Median Absolute Deviation (MAD)0
Skewness-4.8082376
Sum7916031
Variance36364.604
MonotonicityNot monotonic
2024-09-05T21:31:25.931419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
999 7922
96.1%
6 90
 
1.1%
3 86
 
1.0%
4 25
 
0.3%
9 17
 
0.2%
7 16
 
0.2%
2 13
 
0.2%
12 12
 
0.1%
10 8
 
0.1%
5 7
 
0.1%
Other values (13) 42
 
0.5%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 4
 
< 0.1%
2 13
 
0.2%
3 86
1.0%
4 25
 
0.3%
5 7
 
0.1%
6 90
1.1%
7 16
 
0.2%
8 3
 
< 0.1%
9 17
 
0.2%
ValueCountFrequency (%)
999 7922
96.1%
25 1
 
< 0.1%
22 1
 
< 0.1%
21 1
 
< 0.1%
19 1
 
< 0.1%
17 2
 
< 0.1%
16 4
 
< 0.1%
15 5
 
0.1%
14 6
 
0.1%
13 6
 
0.1%

previous
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)0.1%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.18305414
Minimum0
Maximum6
Zeros7060
Zeros (%)85.7%
Negative0
Negative (%)0.0%
Memory size64.5 KiB
2024-09-05T21:31:26.260532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.51420921
Coefficient of variation (CV)2.8090554
Kurtosis20.213808
Mean0.18305414
Median Absolute Deviation (MAD)0
Skewness3.8345994
Sum1508
Variance0.26441112
MonotonicityNot monotonic
2024-09-05T21:31:26.624092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 7060
85.7%
1 947
 
11.5%
2 165
 
2.0%
3 43
 
0.5%
4 14
 
0.2%
5 8
 
0.1%
6 1
 
< 0.1%
(Missing) 2
 
< 0.1%
ValueCountFrequency (%)
0 7060
85.7%
1 947
 
11.5%
2 165
 
2.0%
3 43
 
0.5%
4 14
 
0.2%
5 8
 
0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
6 1
 
< 0.1%
5 8
 
0.1%
4 14
 
0.2%
3 43
 
0.5%
2 165
 
2.0%
1 947
 
11.5%
0 7060
85.7%

poutcome
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size542.6 KiB
nonexistent
7060 
failure
895 
success
 
283

Length

Max length11
Median length11
Mean length10.428017
Min length7

Characters and Unicode

Total characters85906
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownonexistent
2nd rownonexistent
3rd rownonexistent
4th rownonexistent
5th rownonexistent

Common Values

ValueCountFrequency (%)
nonexistent 7060
85.7%
failure 895
 
10.9%
success 283
 
3.4%
(Missing) 2
 
< 0.1%

Length

2024-09-05T21:31:27.405671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-05T21:31:27.800350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
nonexistent 7060
85.7%
failure 895
 
10.9%
success 283
 
3.4%

Most occurring characters

ValueCountFrequency (%)
n 21180
24.7%
e 15298
17.8%
t 14120
16.4%
i 7955
 
9.3%
s 7909
 
9.2%
o 7060
 
8.2%
x 7060
 
8.2%
u 1178
 
1.4%
f 895
 
1.0%
a 895
 
1.0%
Other values (3) 2356
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 85906
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 21180
24.7%
e 15298
17.8%
t 14120
16.4%
i 7955
 
9.3%
s 7909
 
9.2%
o 7060
 
8.2%
x 7060
 
8.2%
u 1178
 
1.4%
f 895
 
1.0%
a 895
 
1.0%
Other values (3) 2356
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 85906
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 21180
24.7%
e 15298
17.8%
t 14120
16.4%
i 7955
 
9.3%
s 7909
 
9.2%
o 7060
 
8.2%
x 7060
 
8.2%
u 1178
 
1.4%
f 895
 
1.0%
a 895
 
1.0%
Other values (3) 2356
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 85906
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 21180
24.7%
e 15298
17.8%
t 14120
16.4%
i 7955
 
9.3%
s 7909
 
9.2%
o 7060
 
8.2%
x 7060
 
8.2%
u 1178
 
1.4%
f 895
 
1.0%
a 895
 
1.0%
Other values (3) 2356
 
2.7%

emp.var.rate
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)0.1%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.056397184
Minimum-3.4
Maximum1.4
Zeros0
Zeros (%)0.0%
Negative3523
Negative (%)42.8%
Memory size64.5 KiB
2024-09-05T21:31:28.156271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-3.4
5-th percentile-2.9
Q1-1.8
median1.1
Q31.4
95-th percentile1.4
Maximum1.4
Range4.8
Interquartile range (IQR)3.2

Descriptive statistics

Standard deviation1.5665504
Coefficient of variation (CV)27.777103
Kurtosis-1.1347579
Mean0.056397184
Median Absolute Deviation (MAD)0.3
Skewness-0.67517435
Sum464.6
Variance2.4540801
MonotonicityNot monotonic
2024-09-05T21:31:28.493748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1.4 3166
38.4%
-1.8 1922
23.3%
1.1 1549
18.8%
-0.1 739
 
9.0%
-2.9 318
 
3.9%
-3.4 203
 
2.5%
-1.7 164
 
2.0%
-1.1 148
 
1.8%
-3 28
 
0.3%
-0.2 1
 
< 0.1%
(Missing) 2
 
< 0.1%
ValueCountFrequency (%)
-3.4 203
 
2.5%
-3 28
 
0.3%
-2.9 318
 
3.9%
-1.8 1922
23.3%
-1.7 164
 
2.0%
-1.1 148
 
1.8%
-0.2 1
 
< 0.1%
-0.1 739
 
9.0%
1.1 1549
18.8%
1.4 3166
38.4%
ValueCountFrequency (%)
1.4 3166
38.4%
1.1 1549
18.8%
-0.1 739
 
9.0%
-0.2 1
 
< 0.1%
-1.1 148
 
1.8%
-1.7 164
 
2.0%
-1.8 1922
23.3%
-2.9 318
 
3.9%
-3 28
 
0.3%
-3.4 203
 
2.5%

cons.price.idx
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)0.3%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean93.570977
Minimum92.201
Maximum94.767
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size64.5 KiB
2024-09-05T21:31:28.850066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum92.201
5-th percentile92.843
Q193.075
median93.444
Q393.994
95-th percentile94.465
Maximum94.767
Range2.566
Interquartile range (IQR)0.919

Descriptive statistics

Standard deviation0.57878242
Coefficient of variation (CV)0.0061854908
Kurtosis-0.86804057
Mean93.570977
Median Absolute Deviation (MAD)0.55
Skewness-0.1854094
Sum770837.71
Variance0.33498909
MonotonicityNot monotonic
2024-09-05T21:31:29.253119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
93.994 1549
18.8%
93.918 1248
15.1%
92.893 1228
14.9%
93.444 1051
12.8%
94.465 867
10.5%
93.2 720
8.7%
93.075 515
 
6.2%
92.963 143
 
1.7%
92.201 142
 
1.7%
92.431 95
 
1.2%
Other values (16) 680
8.3%
ValueCountFrequency (%)
92.201 142
 
1.7%
92.379 48
 
0.6%
92.431 95
 
1.2%
92.469 33
 
0.4%
92.649 60
 
0.7%
92.713 28
 
0.3%
92.756 1
 
< 0.1%
92.843 57
 
0.7%
92.893 1228
14.9%
92.963 143
 
1.7%
ValueCountFrequency (%)
94.767 33
 
0.4%
94.601 42
 
0.5%
94.465 867
10.5%
94.215 71
 
0.9%
94.199 73
 
0.9%
94.055 45
 
0.5%
94.027 48
 
0.6%
93.994 1549
18.8%
93.918 1248
15.1%
93.876 37
 
0.4%

cons.conf.idx
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)0.3%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-40.577907
Minimum-50.8
Maximum-26.9
Zeros0
Zeros (%)0.0%
Negative8238
Negative (%)> 99.9%
Memory size64.5 KiB
2024-09-05T21:31:29.647565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-50.8
5-th percentile-47.1
Q1-42.7
median-41.8
Q3-36.4
95-th percentile-34.6
Maximum-26.9
Range23.9
Interquartile range (IQR)6.3

Descriptive statistics

Standard deviation4.6501009
Coefficient of variation (CV)-0.11459686
Kurtosis-0.36573353
Mean-40.577907
Median Absolute Deviation (MAD)4.4
Skewness0.29346742
Sum-334280.8
Variance21.623438
MonotonicityNot monotonic
2024-09-05T21:31:30.055853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
-36.4 1549
18.8%
-42.7 1248
15.1%
-46.2 1228
14.9%
-36.1 1051
12.8%
-41.8 867
10.5%
-42 720
8.7%
-47.1 515
 
6.2%
-40.8 143
 
1.7%
-31.4 142
 
1.7%
-26.9 95
 
1.2%
Other values (16) 680
8.3%
ValueCountFrequency (%)
-50.8 33
 
0.4%
-50 57
 
0.7%
-49.5 42
 
0.5%
-47.1 515
6.2%
-46.2 1228
14.9%
-45.9 1
 
< 0.1%
-42.7 1248
15.1%
-42 720
8.7%
-41.8 867
10.5%
-40.8 143
 
1.7%
ValueCountFrequency (%)
-26.9 95
 
1.2%
-29.8 48
 
0.6%
-30.1 60
 
0.7%
-31.4 142
 
1.7%
-33 28
 
0.3%
-33.6 33
 
0.4%
-34.6 36
 
0.4%
-34.8 49
 
0.6%
-36.1 1051
12.8%
-36.4 1549
18.8%

euribor3m
Real number (ℝ)

HIGH CORRELATION 

Distinct277
Distinct (%)3.4%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean3.5869295
Minimum0.634
Maximum5.045
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size64.5 KiB
2024-09-05T21:31:30.498667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.634
5-th percentile0.819
Q11.334
median4.857
Q34.961
95-th percentile4.966
Maximum5.045
Range4.411
Interquartile range (IQR)3.627

Descriptive statistics

Standard deviation1.7427836
Coefficient of variation (CV)0.48587061
Kurtosis-1.4745415
Mean3.5869295
Median Absolute Deviation (MAD)0.108
Skewness-0.66214805
Sum29549.125
Variance3.0372947
MonotonicityNot monotonic
2024-09-05T21:31:30.964835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.857 603
 
7.3%
4.963 488
 
5.9%
4.962 486
 
5.9%
4.961 377
 
4.6%
4.964 244
 
3.0%
1.405 224
 
2.7%
4.856 222
 
2.7%
4.864 210
 
2.5%
4.96 208
 
2.5%
4.968 199
 
2.4%
Other values (267) 4977
60.4%
ValueCountFrequency (%)
0.634 2
 
< 0.1%
0.635 8
0.1%
0.636 5
0.1%
0.637 3
 
< 0.1%
0.638 3
 
< 0.1%
0.639 4
 
< 0.1%
0.64 3
 
< 0.1%
0.642 10
0.1%
0.643 3
 
< 0.1%
0.644 7
0.1%
ValueCountFrequency (%)
5.045 4
 
< 0.1%
5 2
 
< 0.1%
4.97 45
 
0.5%
4.968 199
2.4%
4.967 131
 
1.6%
4.966 132
 
1.6%
4.965 193
 
2.3%
4.964 244
3.0%
4.963 488
5.9%
4.962 486
5.9%

nr.employed
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)0.1%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean5165.576
Minimum4963.6
Maximum5228.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size64.5 KiB
2024-09-05T21:31:31.344839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4963.6
5-th percentile5008.7
Q15099.1
median5191
Q35228.1
95-th percentile5228.1
Maximum5228.1
Range264.5
Interquartile range (IQR)129

Descriptive statistics

Standard deviation72.727423
Coefficient of variation (CV)0.014079248
Kurtosis-0.028431039
Mean5165.576
Median Absolute Deviation (MAD)37.1
Skewness-1.0194911
Sum42554015
Variance5289.278
MonotonicityNot monotonic
2024-09-05T21:31:31.707040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
5228.1 3166
38.4%
5099.1 1800
21.8%
5191 1549
18.8%
5195.8 739
 
9.0%
5076.2 318
 
3.9%
5017.5 203
 
2.5%
4991.6 164
 
2.0%
4963.6 148
 
1.8%
5008.7 122
 
1.5%
5023.5 28
 
0.3%
(Missing) 2
 
< 0.1%
ValueCountFrequency (%)
4963.6 148
 
1.8%
4991.6 164
 
2.0%
5008.7 122
 
1.5%
5017.5 203
 
2.5%
5023.5 28
 
0.3%
5076.2 318
 
3.9%
5099.1 1800
21.8%
5176.3 1
 
< 0.1%
5191 1549
18.8%
5195.8 739
9.0%
ValueCountFrequency (%)
5228.1 3166
38.4%
5195.8 739
 
9.0%
5191 1549
18.8%
5176.3 1
 
< 0.1%
5099.1 1800
21.8%
5076.2 318
 
3.9%
5023.5 28
 
0.3%
5017.5 203
 
2.5%
5008.7 122
 
1.5%
4991.6 164
 
2.0%

pmonths
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)0.3%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean960.68744
Minimum0
Maximum999
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size64.5 KiB
2024-09-05T21:31:32.099968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile999
Q1999
median999
Q3999
95-th percentile999
Maximum999
Range999
Interquartile range (IQR)0

Descriptive statistics

Standard deviation191.84101
Coefficient of variation (CV)0.19969139
Kurtosis21.123058
Mean960.68744
Median Absolute Deviation (MAD)0
Skewness-4.8081107
Sum7914143.1
Variance36802.974
MonotonicityNot monotonic
2024-09-05T21:31:32.489686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
999 7922
96.1%
0.2 90
 
1.1%
0.1 86
 
1.0%
0.1333333333 25
 
0.3%
0.3 17
 
0.2%
0.2333333333 16
 
0.2%
0.06666666667 13
 
0.2%
0.4 12
 
0.1%
0.3333333333 8
 
0.1%
0.1666666667 7
 
0.1%
Other values (13) 42
 
0.5%
ValueCountFrequency (%)
0 3
 
< 0.1%
0.03333333333 4
 
< 0.1%
0.06666666667 13
 
0.2%
0.1 86
1.0%
0.1333333333 25
 
0.3%
0.1666666667 7
 
0.1%
0.2 90
1.1%
0.2333333333 16
 
0.2%
0.2666666667 3
 
< 0.1%
0.3 17
 
0.2%
ValueCountFrequency (%)
999 7922
96.1%
0.8333333333 1
 
< 0.1%
0.7333333333 1
 
< 0.1%
0.7 1
 
< 0.1%
0.6333333333 1
 
< 0.1%
0.5666666667 2
 
< 0.1%
0.5333333333 4
 
< 0.1%
0.5 5
 
0.1%
0.4666666667 6
 
0.1%
0.4333333333 6
 
0.1%

pastEmail
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)0.2%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.36550134
Minimum0
Maximum25
Zeros7219
Zeros (%)87.6%
Negative0
Negative (%)0.0%
Memory size64.5 KiB
2024-09-05T21:31:32.860146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum25
Range25
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.2941007
Coefficient of variation (CV)3.5406182
Kurtosis55.498133
Mean0.36550134
Median Absolute Deviation (MAD)0
Skewness6.0550466
Sum3011
Variance1.6746965
MonotonicityNot monotonic
2024-09-05T21:31:33.270154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 7219
87.6%
2 322
 
3.9%
1 251
 
3.0%
3 180
 
2.2%
4 125
 
1.5%
6 43
 
0.5%
5 38
 
0.5%
8 22
 
0.3%
12 12
 
0.1%
9 8
 
0.1%
Other values (7) 18
 
0.2%
ValueCountFrequency (%)
0 7219
87.6%
1 251
 
3.0%
2 322
 
3.9%
3 180
 
2.2%
4 125
 
1.5%
5 38
 
0.5%
6 43
 
0.5%
7 2
 
< 0.1%
8 22
 
0.3%
9 8
 
0.1%
ValueCountFrequency (%)
25 1
 
< 0.1%
18 2
 
< 0.1%
16 2
 
< 0.1%
15 3
 
< 0.1%
14 1
 
< 0.1%
12 12
0.1%
10 7
 
0.1%
9 8
 
0.1%
8 22
0.3%
7 2
 
< 0.1%

responded
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size16.2 KiB
False
7310 
True
928 
(Missing)
 
2
ValueCountFrequency (%)
False 7310
88.7%
True 928
 
11.3%
(Missing) 2
 
< 0.1%
2024-09-05T21:31:33.633796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

profit
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct380
Distinct (%)40.9%
Missing7310
Missing (%)88.7%
Infinite0
Infinite (%)0.0%
Mean77.709677
Minimum-87622.112
Maximum515
Zeros1
Zeros (%)< 0.1%
Negative76
Negative (%)0.9%
Memory size64.5 KiB
2024-09-05T21:31:34.036493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-87622.112
5-th percentile-30.55
Q1124
median170
Q3213
95-th percentile443.55
Maximum515
Range88137.112
Interquartile range (IQR)89

Descriptive statistics

Standard deviation2881.7685
Coefficient of variation (CV)37.083779
Kurtosis926.27267
Mean77.709677
Median Absolute Deviation (MAD)45
Skewness-30.404267
Sum72270
Variance8304589.7
MonotonicityNot monotonic
2024-09-05T21:31:34.495032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178 12
 
0.1%
162 10
 
0.1%
151 10
 
0.1%
160 10
 
0.1%
163 10
 
0.1%
176 9
 
0.1%
171 9
 
0.1%
185 8
 
0.1%
168 8
 
0.1%
124 8
 
0.1%
Other values (370) 836
 
10.1%
(Missing) 7310
88.7%
ValueCountFrequency (%)
-87622.11207 1
< 0.1%
-276 1
< 0.1%
-224 1
< 0.1%
-205 1
< 0.1%
-202 1
< 0.1%
-187 1
< 0.1%
-180 1
< 0.1%
-177 2
< 0.1%
-174 1
< 0.1%
-173 1
< 0.1%
ValueCountFrequency (%)
515 1
< 0.1%
509 1
< 0.1%
507 1
< 0.1%
501 1
< 0.1%
500 1
< 0.1%
499 1
< 0.1%
497 1
< 0.1%
496 2
< 0.1%
495 1
< 0.1%
494 1
< 0.1%

id
Real number (ℝ)

HIGH CORRELATION  UNIFORM 

Distinct8238
Distinct (%)100.0%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean4119.5
Minimum1
Maximum8238
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size64.5 KiB
2024-09-05T21:31:34.937193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile412.85
Q12060.25
median4119.5
Q36178.75
95-th percentile7826.15
Maximum8238
Range8237
Interquartile range (IQR)4118.5

Descriptive statistics

Standard deviation2378.2501
Coefficient of variation (CV)0.57731523
Kurtosis-1.2
Mean4119.5
Median Absolute Deviation (MAD)2059.5
Skewness0
Sum33936441
Variance5656073.5
MonotonicityStrictly increasing
2024-09-05T21:31:35.378665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5536 1
 
< 0.1%
5502 1
 
< 0.1%
5501 1
 
< 0.1%
5500 1
 
< 0.1%
5499 1
 
< 0.1%
5498 1
 
< 0.1%
5497 1
 
< 0.1%
5496 1
 
< 0.1%
5495 1
 
< 0.1%
5494 1
 
< 0.1%
Other values (8228) 8228
99.9%
(Missing) 2
 
< 0.1%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
8238 1
< 0.1%
8237 1
< 0.1%
8236 1
< 0.1%
8235 1
< 0.1%
8234 1
< 0.1%
8233 1
< 0.1%
8232 1
< 0.1%
8231 1
< 0.1%
8230 1
< 0.1%
8229 1
< 0.1%

Interactions

2024-09-05T21:31:08.532901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:14.677580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:19.490977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:24.009654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:28.269522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:32.565526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:36.763450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:41.075462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:45.628306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:50.574472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:54.899632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:59.266804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:31:03.503577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:31:08.864803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:15.161466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:19.849376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:24.355566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:28.631279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:32.899867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:37.101356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:41.417554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:45.976593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:50.933759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:55.242329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:59.606651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:31:03.862859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:31:09.198317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:15.570456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:20.230067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:24.720001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:29.010554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:33.241212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:37.458573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:41.764798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:46.315392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:51.286288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:55.603755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:59.962338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:31:04.254993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:31:09.514987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:15.901722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:20.569000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:25.030288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:29.317755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:33.554490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:37.791795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:42.425369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:46.731682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:51.608026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:55.930075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:31:00.275188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:31:04.597368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:31:09.854456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:16.237531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:20.908870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:25.374532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:29.654216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:33.891124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:38.134539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:42.742761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:47.227071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:51.944775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:56.260869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:31:00.584925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:31:04.964328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:31:10.179694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:16.563279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:21.238591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:25.687164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:29.979405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:34.196408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:38.459454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:43.059437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:47.653847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:52.268836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:56.573583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:31:00.909463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:31:05.306829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:31:10.500691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:16.890704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:21.582415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:26.008727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:30.290789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:34.498064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:38.777389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:43.384084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:48.051859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:52.589123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:56.986427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:31:01.240810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:31:05.664296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:31:10.799585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:17.214340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:21.906846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:26.314381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:30.610020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:34.807657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:39.087105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:43.692750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:48.426213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:52.909417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:57.312206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:31:01.551381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:31:06.028233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:31:11.100761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:17.520853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:22.239260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:26.623058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:30.914981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:35.117317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:39.404003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:44.001070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:48.823272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:53.251077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:57.633508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:31:01.863134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:31:06.397863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:31:11.423739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:17.841568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:22.588138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:26.940557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:31.235495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:35.414784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:39.726984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:44.307750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:49.211246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:53.545096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:57.933010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:31:02.187437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:31:06.757201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:31:11.741125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:18.171286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:22.955389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:27.256375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:31.562991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:35.730003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:40.063114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:44.623927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:49.577996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:53.871588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:58.281729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:31:02.494393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:31:07.113509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:31:12.079792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:18.505240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:23.285398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:27.576110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:31.881856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:36.091536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:40.370534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:44.922664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:49.887062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:54.195194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:58.581853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:31:02.803478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:31:07.526334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:31:12.470038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:19.133422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:23.686912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:27.948864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:32.248812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:36.465943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:40.776132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:45.317525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:50.262035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:54.582058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:30:58.962898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:31:03.195799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-05T21:31:08.164122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-09-05T21:31:35.781228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
campaigncons.conf.idxcons.price.idxcontactcustAgeday_of_weekdefaultemp.var.rateeuribor3mhousingidloanmaritalmonthnr.employedpastEmailpdayspmonthspoutcomepreviousprofessionprofitrespondedschooling
campaign1.000-0.0120.1010.085-0.0010.0050.0000.1500.1270.000-0.0410.0000.0000.0450.137-0.0650.0390.0390.041-0.0730.013-0.0560.0510.018
cons.conf.idx-0.0121.0000.2690.4160.1220.0440.1260.2680.2720.034-0.0020.0000.0560.5970.172-0.140-0.072-0.0720.366-0.1360.1040.0670.3700.065
cons.price.idx0.1010.2691.0000.6800.0580.0490.1430.6700.4900.065-0.0620.0110.0640.6760.460-0.2620.0520.0520.394-0.2750.126-0.0840.3230.097
contact0.0850.4160.6801.0000.0320.0540.1240.2410.1540.073-0.0650.0000.0470.6020.119-0.2220.1180.1180.240-0.2400.123-0.1110.1440.116
custAge-0.0010.1220.0580.0321.0000.0240.1410.0460.0560.000-0.0170.0000.2670.1090.040-0.009-0.008-0.0080.125-0.0020.2410.2190.1770.126
day_of_week0.0050.0440.0490.0540.0241.0000.0000.0190.0190.020-0.0120.0120.0070.0650.016-0.0030.0040.0040.007-0.0040.0190.0540.0220.029
default0.0000.1260.1430.1240.1410.0001.0000.1640.1570.006-0.0540.0000.0910.1010.147-0.0920.0770.0770.077-0.1030.143-0.4460.0910.146
emp.var.rate0.1500.2680.6700.2410.0460.0190.1641.0000.9380.043-0.1500.0080.0560.6610.941-0.3990.2290.2290.388-0.4340.126-0.1350.3430.069
euribor3m0.1270.2720.4900.1540.0560.0190.1570.9381.0000.039-0.1630.0090.0580.6500.930-0.4200.2860.2860.424-0.4620.142-0.1710.3940.072
housing0.0000.0340.0650.0730.0000.0200.0060.0430.0391.000-0.0010.7080.0170.048-0.0260.0200.0000.0000.0120.0170.0000.0110.0060.000
id-0.041-0.002-0.062-0.065-0.017-0.012-0.054-0.150-0.163-0.0011.0000.0000.0260.091-0.1700.095-0.189-0.1890.2290.1150.044-0.0580.9430.014
loan0.0000.0000.0110.0000.0000.0120.0000.0080.0090.7080.0001.0000.0140.0000.014-0.004-0.010-0.0100.007-0.0060.0060.5320.0090.012
marital0.0000.0560.0640.0470.2670.0070.0910.0560.0580.0170.0260.0141.0000.038-0.0610.016-0.034-0.0340.0240.0160.182-0.0990.0500.116
month0.0450.5970.6760.6020.1090.0650.1010.6610.6500.0480.0910.0000.0381.000-0.3590.130-0.061-0.0610.2510.1460.1070.0120.2900.096
nr.employed0.1370.1720.4600.1190.0400.0160.1470.9410.930-0.026-0.1700.014-0.061-0.3591.000-0.4000.2970.2970.412-0.4400.127-0.1740.4040.070
pastEmail-0.065-0.140-0.262-0.222-0.009-0.003-0.092-0.399-0.4200.0200.095-0.0040.0160.130-0.4001.000-0.466-0.4660.4360.9170.0480.0960.1470.031
pdays0.039-0.0720.0520.118-0.0080.0040.0770.2290.2860.000-0.189-0.010-0.034-0.0610.297-0.4661.0001.0000.946-0.5090.141-0.1050.3370.044
pmonths0.039-0.0720.0520.118-0.0080.0040.0770.2290.2860.000-0.189-0.010-0.034-0.0610.297-0.4661.0001.0000.946-0.5090.141-0.1050.3370.044
poutcome0.0410.3660.3940.2400.1250.0070.0770.3880.4240.0120.2290.0070.0240.2510.4120.4360.9460.9461.000-0.5030.1060.0380.3320.042
previous-0.073-0.136-0.275-0.240-0.002-0.004-0.103-0.434-0.4620.0170.115-0.0060.0160.146-0.4400.917-0.509-0.509-0.5031.0000.0600.1000.2230.024
profession0.0130.1040.1260.1230.2410.0190.1430.1260.1420.0000.0440.0060.1820.1070.1270.0480.1410.1410.1060.0601.0000.0910.1460.362
profit-0.0560.067-0.084-0.1110.2190.054-0.446-0.135-0.1710.011-0.0580.532-0.0990.012-0.1740.096-0.105-0.1050.0380.1000.0911.0001.0001.000
responded0.0510.3700.3230.1440.1770.0220.0910.3430.3940.0060.9430.0090.0500.2900.4040.1470.3370.3370.3320.2230.1461.0001.0000.060
schooling0.0180.0650.0970.1160.1260.0290.1460.0690.0720.0000.0140.0120.1160.0960.0700.0310.0440.0440.0420.0240.3621.0000.0601.000

Missing values

2024-09-05T21:31:12.986104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-05T21:31:14.063341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-09-05T21:31:15.285574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

custAgeprofessionmaritalschoolingdefaulthousingloancontactmonthday_of_weekcampaignpdayspreviouspoutcomeemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedpmonthspastEmailrespondedprofitid
034.0admin.singleuniversity.degreenonoyescellularaprwed2.0999.00.0nonexistent-1.893.075-47.11.4985099.1999.00.0noNaN1.0
131.0servicessinglehigh.schoolnononocellularjulthu35.0999.00.0nonexistent1.493.918-42.74.9685228.1999.00.0noNaN2.0
2NaNadmin.singlehigh.schoolnononotelephonejunNaN1.0999.00.0nonexistent1.494.465-41.84.9615228.1999.00.0noNaN3.0
352.0admin.divorceduniversity.degreeunknownyesnocellularjultue2.0999.00.0nonexistent1.493.918-42.74.9625228.1999.00.0noNaN4.0
439.0blue-collarsingleNaNunknownyesnocellularjultue6.0999.00.0nonexistent1.493.918-42.74.9615228.1999.00.0noNaN5.0
540.0entrepreneurmarriedNaNnoyesnotelephonejunthu3.0999.00.0nonexistent1.494.465-41.84.8665228.1999.00.0noNaN6.0
650.0techniciansingleNaNnononocellularjultue3.0999.00.0nonexistent1.493.918-42.74.9615228.1999.00.0noNaN7.0
741.0technicianmarriedprofessional.coursenononocellularoctthu2.0999.00.0nonexistent-3.492.431-26.90.7415017.5999.00.0noNaN8.0
823.0blue-collarsinglebasic.4ynoyesnotelephonejunfri10.0999.00.0nonexistent1.494.465-41.84.9595228.1999.00.0noNaN9.0
929.0technicianmarriedprofessional.coursenoyesnocellularaugmon3.0999.00.0nonexistent1.493.444-36.14.9655228.1999.00.0noNaN10.0
custAgeprofessionmaritalschoolingdefaulthousingloancontactmonthday_of_weekcampaignpdayspreviouspoutcomeemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedpmonthspastEmailrespondedprofitid
823028.0admin.marrieduniversity.degreenononocellularaprthu1.02.02.0success-1.893.075-47.11.3655099.10.0666670.0yes193.0000008231.0
823134.0admin.singleuniversity.degreenoyesnocellularaugwed1.0999.00.0nonexistent1.493.444-36.14.9655228.1999.0000000.0yes176.0000008232.0
823252.0servicesmarriedhigh.schoolunknownyesnocellularjulfri3.0999.00.0nonexistent1.493.918-42.74.9625228.1999.0000000.0yes-15.0000008233.0
823355.0retiredmarriedhigh.schoolnoyesnocellularoctthu2.07.01.0success-3.492.431-26.90.7225017.50.2333330.0yes203.0000008234.0
823441.0admin.divorcedhigh.schoolnononotelephonejunmon11.0999.00.0nonexistent1.494.465-41.84.9605228.1999.0000000.0yes188.0000008235.0
823532.0self-employedsingleuniversity.degreenononocellularaprthu1.0999.00.0nonexistent-1.893.075-47.11.4355099.1999.0000000.0yes208.0000008236.0
8236NaNhousemaidmarrieduniversity.degreenononocellularjuntue1.0999.00.0nonexistent-2.992.963-40.81.0995076.2999.0000000.0yes129.0000008237.0
823785.0housemaidmarriedNaNunknownyesnocellularaprtue1.0999.01.0failure-1.893.749-34.60.6425008.7999.0000001.0yes33.0000008238.0
8238NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN172.112069NaN
8239NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN-87622.112070NaN